Fintech For The Poor: Financial Intermediation Without Discrimination

I ask whether machine learning algorithms improve the efficiency in lending without compromising on equity in a credit environment where soft information dominates. I obtain loan application-level data from an Indian bank. To overcome the problem of the selective labels, I exploit the incentive-driven within officer difference in leniency within a calendar month. I find that the ML algorithm can lend 60% more at loan officers’ delinquency rate or achieve a 33% lower delinquency rate at loan officers’ approval rate. The efficiency is maintained even when the algorithm is explicitly prevented from discriminating against disadvantaged social classes.

Are women mayors more effective in reducing the crime rate? Sharp regression discontinuity evidence from Peru

Did women mayors improve local security in their districts as compared to their male counterparts? Our analysis is focused on the impacts that women mayors had on the delinquency rate in Peru between 2015 and 2017. We apply sharp regression discontinuity (SRD) on official government data to estimate impacts. Despite the relatively small number of districts with female mayors, SRD passed all required assumptions and falsification tests. Our results indicate that female leadership in municipalities can reduce delinquency rate in 9 cases per 1,000 inhabitants in Peruvian districts, starting from the second year of the 4-year mandatory period. These findings complement those in recent literature that emphasize the role of women in public management.

The Determinants of the Confidi Delinquency Rate

This study is focused on the Italian confidi, the financial institutions placed in an intermediate position between lenders and borrowers, because they offer guarantees care of banks to the associated SMEs. During the last years some of the most important guarantee institutions failed, because of bad and doubtful credits. The main aim of the research is to determine the responsible factors about their delinquency rate. The analysis is realized on 99 confidi during 2006-2015 (10 years). The data about 2016 are not usable, because the Italian confidi are living a change in the accountings standards, making it impossible a comparison between the balance sheets. The regression model show that the difficulties in credit collections essentially depend to the economic crisis and to the bad management of the organizations. We cannot also forget the practical and social implications of the study. The forecasting of the riskier confidi will help the Italian policy makers both in the individuation of correct instruments of rescue and in finding a solution for another important problem: the unemployment.